One of the most challenging aspects of enzyme engineering is deducing the correlations between the residues of a protein. Previous methods to find correlations between residues investigated the collective motion of protein residues and have relied on principle component analysis, in which a covariance matrix is evaluated. Such methods are of limited usefulness, as they identify correlated residue movements only in the same (parallel) or opposite (anti-parallel) direction. These previous methods are also hampered by having low sensitivity for identifying correlated residue networks (Harte, W. E., et. al., 1990, PNAS, 85, 4686; Ichiye, T. and Karplus, M., 1991, Protein Struct. Funct. Genet. 11:205).
Other methods to find correlations between residues in a protein use bioinformatics based approaches (for example Fodor et. al., 2004, Proteins, 56,211 and citations therein). These methods rely on the analysis of large sequence alignments and statistical analysis of correlated mutations. However, it has been shown that statistical coupling between two positions in a protein based on sequence alignments is not necessarily reflected in actual thermodynamic coupling as determined experimentally using double mutant cycles (Chi et. al., 2008, PNAS, 105,12).